Sentinel-1 for Monitoring Land Subsidence of Coastal Cities in Africa Using PSInSAR: A Methodology Based on the Integration of SNAP and StaMPS

Abstract

The sub-Saharan African coast is experiencing fast-growing urbanization, particularly around major cities. This threatens the equilibrium of the socio-ecosystems where they are located and on which they depend: underground water resources are exploited with a disregard for sustainability; land is reclaimed from wetlands or lagoons; built-up areas, both formal and informal, grow without adequate urban planning. Together, all these forces can result in land surface deformation, subsidence or even uplift, which can increase risk within these already fragile socio-ecosystems. In particular, in the case of land subsidence, the risk of urban flooding can increase significantly, also considering the contribution of sea level rise driven by climate change. Monitoring such fast-changing environments is crucial to be able to identify key risks and plan adaptation responses to mitigate current and future flood risks. Persistent scatterer interferometry (PSI) with synthetic aperture radar (SAR) is a powerful tool to monitor land deformation with high precision using relatively low-cost technology, also thanks to the open access data of Sentinel-1, which provides global observations every 6 days at 20-m ground resolution. In this paper, we demonstrate how it is possible to monitor land subsidence in urban coastal areas by means of permanent scatterer interferometry and Sentinel-1, exploiting an automatic procedure based on an integration of the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). We present the results of PSI analysis over the cities of Banjul (the Gambia) and Lagos (Nigeria) showing a comparison of results obtained with TerraSAR-X, Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO-SkyMed) and Environmental Satellite advanced synthetic aperture radar (Envisat-ASAR) data. The methodology allows us to highlight areas of high land deformation, information that is useful for urban development, disaster risk management and climate adaptation planning.